Two of the most interesting things potentially ever are happening in our lifetime: the rise of machine learning and the blockchain revolution.
Machine Learning (ML) systems have been able to surpass humans in many problem domains. These systems are now better at lip reading, speech recognition, location tagging, playing Go, image classification, and more.
With the invention of the blockchain and bitcoin, we’ve seen a wave of new cryptocurrencies and distributed applications built on these new blockchains.
The DanKu protocol is an overlap between the blockchain and Machine Learning. It helps facilitate exchanging ML models on the Ethereum blockchain. We even published a whitepaper about it here. You can read more about the DanKu protocol in our previous blog post.
Machine Learning algorithms are being developed and improved at an incredible rate, but are not necessarily getting more accessible to the broader community. That’s why today Algorithmia is announcing DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. DanKu enables anyone to get access to high quality, objectively measured machine learning models. At Algorithmia, we believe that widespread access to algorithms and deployment solutions is going to be a fundamental building block of a balanced future for AI, and DanKu is a step towards that vision.
The DanKu protocol utilizes blockchain technology via smart contracts. The contract allows anyone to post a data set, an evaluation function, and a monetary reward for anyone who can provide the best trained machine learning model for the data. Participants train deep neural networks to model the data, and submit their trained networks to the blockchain. The blockchain executes these neural network models to evaluate submissions, and ensure that payment goes to the best model.
The contract allows for the creation of a decentralized and trustless marketplace for exchanging ML models. This gives ML practitioners an opportunity to monetize their skills directly. It also allows any participant or organization to solicit machine learning models from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents. Anyone with a dataset, including software agents can create DanKu contracts.
We’re also launching the first DanKu competition for a machine learning problem. Read More…
We host more than 4000 algorithms for over 50k developers. Here is a list of best practices we’ve identified for designing advanced algorithms. We hope this can help you and your team. Read More…
We often get asked about if we’re planning on adding any non-English NLP algorithms. As much as we would love to train NLP models on other languages, there aren’t many usable training datasets in these languages. And, due to the linguistic structure of these languages, training with pre-existing approaches doesn’t always give the best results.
Until better training sets can be generated, one passable solution is to translate the text to English before sending it to the algorithm. Read More…
Your website publishes thousands of articles each day. Your writers create stories, embed images, and tag them for SEO purposes. It’s your job to share them out on social media… but you’re struggling to keep up with the volume.
After coming up with a snappy tagline, you still have to select the best image and crop it to different sizes for Facebook, Twitter, LinkedIn, and all the other networks. Using a batch image-cropper might remove something important from the photo — like Elon Musk’s face, or half of the car being featured — so you put in a lot of time cropping and resizing by hand.
What if you had an automated way of handling the image picking and cropping process? Well, there’s now an algorithm for that. Today we’ll talk about how we’ve managed to bring together many different algorithms into a single ensemble that can intelligently select, crop, and score images for social media sharing.